初始化仓库，已经实现了normal和avx2的sw，并进行了性能测试
This commit is contained in:
zzh
commit
2904e87dee
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*.[oa]
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sw_perf
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test
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test64
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.*.swp
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Makefile.bak
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bwamem-lite
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# ---> C
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# Prerequisites
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*.d
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# Object files
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*.o
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*.ko
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*.obj
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*.elf
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# Linker output
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*.ilk
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*.map
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*.exp
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# Precompiled Headers
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*.gch
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*.pch
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# Libraries
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*.lib
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*.a
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*.la
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*.lo
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# Shared objects (inc. Windows DLLs)
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*.dll
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*.so
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*.so.*
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*.dylib
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# Executables
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*.exe
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*.out
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*.app
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*.i*86
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*.x86_64
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*.hex
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# Debug files
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*.dSYM/
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*.su
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*.idb
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*.pdb
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# Kernel Module Compile Results
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*.mod*
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*.cmd
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.tmp_versions/
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modules.order
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Module.symvers
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Mkfile.old
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dkms.conf
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{
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// 使用 IntelliSense 了解相关属性。
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// 悬停以查看现有属性的描述。
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// 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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{
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"name": "sw-perf",
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"preLaunchTask": "Build",
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"type": "cppdbg",
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"request": "launch",
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"program": "${workspaceRoot}/sw_perf",
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"args": [
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"all"
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],
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"cwd": "${workspaceFolder}", // 当前工作路径：当前文件所在的工作空间
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}
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]
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}
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{
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"files.associations": {
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"functional": "c"
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}
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}
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{
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// See https://go.microsoft.com/fwlink/?LinkId=733558
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// for the documentation about the tasks.json format
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"version": "2.0.0",
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"tasks": [
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{
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"label": "Build",
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"type": "shell",
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"command": "make clean; make -j 16",
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"problemMatcher": [],
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"group": {
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"kind": "build",
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"isDefault": true
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}
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}
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]
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}
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CC= gcc
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#CFLAGS= -g -Wall -Wno-unused-function
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CFLAGS= -Wall -Wno-unused-function -O2 -mavx2
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DFLAGS= -DSHOW_PERF
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OBJS= ksw_normal.o ksw_avx2.o ksw_cuda.o ksw_avx2_u8.o
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PROG= sw_perf
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INCLUDES=
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LIBS=
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SUBDIRS= .
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ifeq ($(shell uname -s),Linux)
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LIBS += -lrt
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endif
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.SUFFIXES:.c .o .cc
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.c.o:
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$(CC) -c $(CFLAGS) $(DFLAGS) $(INCLUDES) $(CPPFLAGS) $< -o $@
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all:$(PROG)
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sw_perf:$(OBJS) main.o
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$(CC) $(CFLAGS) $(LDFLAGS) $(OBJS) main.o -o $@ -L. $(LIBS)
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clean:
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rm -f *.o a.out $(PROG) *~ *.a
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depend:
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( LC_ALL=C ; export LC_ALL; makedepend -Y -- $(CFLAGS) $(DFLAGS) $(CPPFLAGS) -- *.c )
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# DO NOT DELETE THIS LINE -- make depend depends on it.
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#include <stdlib.h>
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#include <stdint.h>
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#include <assert.h>
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#include <emmintrin.h>
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#include <stdio.h>
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#include <immintrin.h>
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#include <emmintrin.h>
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#ifdef __GNUC__
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#define LIKELY(x) __builtin_expect((x), 1)
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#define UNLIKELY(x) __builtin_expect((x), 0)
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#else
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#define LIKELY(x) (x)
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#define UNLIKELY(x) (x)
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#endif
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#undef MAX
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#undef MIN
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#define MAX(x, y) ((x) > (y) ? (x) : (y))
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#define MIN(x, y) ((x) < (y) ? (x) : (y))
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#define SIMD_WIDTH 16
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int ksw_extend2_origin(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int is_left, int m, const int8_t *mat, int o_del, int e_del,
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int o_ins, int e_ins, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off);
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static const uint16_t h_vec_int_mask[SIMD_WIDTH][SIMD_WIDTH] = {
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{0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0},
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{0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff, 0xffff}};
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// const int permute_mask = _MM_SHUFFLE(0, 1, 2, 3);
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#define permute_mask _MM_SHUFFLE(0, 1, 2, 3)
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// 初始化变量
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#define SIMD_INIT \
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int oe_del = o_del + e_del, oe_ins = o_ins + e_ins; \
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__m256i zero_vec; \
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__m256i max_vec; \
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__m256i oe_del_vec; \
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__m256i oe_ins_vec; \
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__m256i e_del_vec; \
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__m256i e_ins_vec; \
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__m256i h_vec_mask[SIMD_WIDTH]; \
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zero_vec = _mm256_setzero_si256(); \
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oe_del_vec = _mm256_set1_epi16(-oe_del); \
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oe_ins_vec = _mm256_set1_epi16(-oe_ins); \
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e_del_vec = _mm256_set1_epi16(-e_del); \
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e_ins_vec = _mm256_set1_epi16(-e_ins); \
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__m256i match_sc_vec = _mm256_set1_epi16(a); \
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__m256i mis_sc_vec = _mm256_set1_epi16(-b); \
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__m256i amb_sc_vec = _mm256_set1_epi16(-1); \
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__m256i amb_vec = _mm256_set1_epi16(4); \
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for (i = 0; i < SIMD_WIDTH; ++i) \
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h_vec_mask[i] = _mm256_loadu_si256((__m256i *)(&h_vec_int_mask[i]));
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/*
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* e 表示当前ref的碱基被删除
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* f 表示当前seq的碱基插入
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* m 表示当前碱基匹配（可以相等，也可以不想等）
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* h 表示最大值
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*/
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// load向量化数据
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#define SIMD_LOAD \
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__m256i m1 = _mm256_loadu_si256((__m256i *)(&mA1[j])); \
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__m256i e1 = _mm256_loadu_si256((__m256i *)(&eA1[j])); \
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__m256i m1j1 = _mm256_loadu_si256((__m256i *)(&mA1[j - 1])); \
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__m256i f1j1 = _mm256_loadu_si256((__m256i *)(&fA1[j - 1])); \
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__m256i h0j1 = _mm256_loadu_si256((__m256i *)(&hA0[j - 1])); \
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__m256i qs_vec = _mm256_loadu_si256((__m256i *)(&seq[j - 1])); \
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__m256i ts_vec = _mm256_loadu_si256((__m256i *)(&ref[i]));
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// 比对ref和seq的序列，计算罚分
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#define SIMD_CMP_SEQ \
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ts_vec = _mm256_permute4x64_epi64(ts_vec, permute_mask); \
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ts_vec = _mm256_shufflelo_epi16(ts_vec, permute_mask); \
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ts_vec = _mm256_shufflehi_epi16(ts_vec, permute_mask); \
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__m256i match_mask_vec = _mm256_cmpeq_epi16(qs_vec, ts_vec); \
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__m256i mis_score_vec = _mm256_andnot_si256(match_mask_vec, mis_sc_vec); \
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__m256i score_vec = _mm256_and_si256(match_sc_vec, match_mask_vec); \
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score_vec = _mm256_or_si256(score_vec, mis_score_vec); \
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__m256i q_amb_mask_vec = _mm256_cmpeq_epi16(qs_vec, amb_vec); \
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__m256i t_amb_mask_vec = _mm256_cmpeq_epi16(ts_vec, amb_vec); \
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__m256i amb_mask_vec = _mm256_or_si256(q_amb_mask_vec, t_amb_mask_vec); \
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score_vec = _mm256_andnot_si256(amb_mask_vec, score_vec); \
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__m256i amb_score_vec = _mm256_and_si256(amb_mask_vec, amb_sc_vec); \
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score_vec = _mm256_or_si256(score_vec, amb_score_vec);
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// 向量化计算h, e, f, m
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#define SIMD_COMPUTE \
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__m256i en_vec0 = _mm256_add_epi16(m1, oe_del_vec); \
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__m256i en_vec1 = _mm256_add_epi16(e1, e_del_vec); \
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__m256i en_vec = _mm256_max_epi16(en_vec0, en_vec1); \
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__m256i fn_vec0 = _mm256_add_epi16(m1j1, oe_ins_vec); \
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__m256i fn_vec1 = _mm256_add_epi16(f1j1, e_ins_vec); \
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__m256i fn_vec = _mm256_max_epi16(fn_vec0, fn_vec1); \
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__m256i mn_vec0 = _mm256_add_epi16(h0j1, score_vec); \
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__m256i mn_mask = _mm256_cmpgt_epi16(h0j1, zero_vec); \
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__m256i mn_vec = _mm256_and_si256(mn_vec0, mn_mask); \
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__m256i hn_vec0 = _mm256_max_epi16(en_vec, fn_vec); \
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__m256i hn_vec = _mm256_max_epi16(hn_vec0, mn_vec); \
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en_vec = _mm256_max_epi16(en_vec, zero_vec); \
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fn_vec = _mm256_max_epi16(fn_vec, zero_vec); \
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mn_vec = _mm256_max_epi16(mn_vec, zero_vec); \
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hn_vec = _mm256_max_epi16(hn_vec, zero_vec);
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// 存储向量化结果
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#define SIMD_STORE \
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max_vec = _mm256_max_epi16(max_vec, hn_vec); \
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_mm256_storeu_si256((__m256i *)&eA2[j], en_vec); \
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_mm256_storeu_si256((__m256i *)&fA2[j], fn_vec); \
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_mm256_storeu_si256((__m256i *)&mA2[j], mn_vec); \
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_mm256_storeu_si256((__m256i *)&hA2[j], hn_vec);
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// 去除多余的部分
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#define SIMD_REMOVE_EXTRA \
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en_vec = _mm256_and_si256(en_vec, h_vec_mask[end - j]); \
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fn_vec = _mm256_and_si256(fn_vec, h_vec_mask[end - j]); \
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mn_vec = _mm256_and_si256(mn_vec, h_vec_mask[end - j]); \
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hn_vec = _mm256_and_si256(hn_vec, h_vec_mask[end - j]);
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// 找最大值和位置
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#define SIMD_FIND_MAX \
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max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 2)); \
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max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 4)); \
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max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 6)); \
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max_vec = _mm256_max_epu16(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 8)); \
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max_vec = _mm256_max_epu16(max_vec, _mm256_permute2x128_si256(max_vec, max_vec, 0x01)); \
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int16_t *maxVal = (int16_t *)&max_vec; \
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m = maxVal[0]; \
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if (m > 0) \
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{ \
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for (j = beg, i = iend; j <= end; j += SIMD_WIDTH, i -= SIMD_WIDTH) \
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{ \
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__m256i h2_vec = _mm256_loadu_si256((__m256i *)(&hA2[j])); \
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__m256i vcmp = _mm256_cmpeq_epi16(h2_vec, max_vec); \
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uint32_t mask = _mm256_movemask_epi8(vcmp); \
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||||
if (mask > 0) \
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||||
{ \
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||||
int pos = SIMD_WIDTH - 1 - ((__builtin_clz(mask)) >> 1); \
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mj = j - 1 + pos; \
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mi = i - 1 - pos; \
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||||
} \
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||||
} \
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}
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||||
// 每轮迭代后，交换数组
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||||
#define SWAP_DATA_POINTER \
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||||
int16_t *tmp = hA0; \
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||||
hA0 = hA1; \
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||||
hA1 = hA2; \
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hA2 = tmp; \
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||||
tmp = eA1; \
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eA1 = eA2; \
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eA2 = tmp; \
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tmp = fA1; \
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||||
fA1 = fA2; \
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||||
fA2 = tmp; \
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tmp = mA1; \
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mA1 = mA2; \
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mA2 = tmp;
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int ksw_avx2(int qlen, // query length 待匹配段碱基的query长度
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const uint8_t *query, // read碱基序列
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int tlen, // target length reference的长度
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const uint8_t *target, // reference序列
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int is_left, // 是不是向左扩展
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||||
int m, // 碱基种类 (5)
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||||
const int8_t *mat, // 每个位置的query和target的匹配得分 m*m
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int o_del, // deletion 错配开始的惩罚系数
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||||
int e_del, // deletion extension的惩罚系数
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int o_ins, // insertion 错配开始的惩罚系数
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||||
int e_ins, // insertion extension的惩罚系数SIMD_BTYES
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||||
int a, // 碱基match时的分数
|
||||
int b, // 碱基mismatch时的惩罚分数（正数）
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||||
int w, // 提前剪枝系数，w =100 匹配位置和beg的最大距离
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||||
int end_bonus,
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||||
int zdrop,
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||||
int h0, // 该seed的初始得分（完全匹配query的碱基数）
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||||
int *_qle, // 匹配得到全局最大得分的碱基在query的位置
|
||||
int *_tle, // 匹配得到全局最大得分的碱基在reference的位置
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int *_gtle, // query全部匹配上的target的长度
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int *_gscore, // query的端到端匹配得分
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||||
int *_max_off) // 取得最大得分时在query和reference上位置差的 最大值
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{
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||||
// ksw_extend2_origin
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// return ksw_extend2_origin(qlen, query, tlen, target, is_left, m, mat, o_del, e_del, o_ins, e_ins, w, end_bonus, zdrop, h0, _qle, _tle, _gtle, _gscore, _max_off);
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||||
// fprintf(stderr, "qlen: %d, tlen: %d\n", qlen, tlen);
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||||
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||||
// if (qlen * a + h0 < 255)
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||||
// return ksw_extend2_avx2_u8(qlen, query, tlen, target, is_left, m, mat, o_del, e_del, o_ins, e_ins, a, b, w, end_bonus, zdrop, h0, _qle, _tle, _gtle, _gscore, _max_off);
|
||||
|
||||
int16_t *mA, *hA, *eA, *fA, *mA1, *mA2, *hA0, *hA1, *eA1, *fA1, *hA2, *eA2, *fA2; // hA0保存上上个col的H，其他的保存上个H E F M
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||||
int16_t *seq, *ref;
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||||
uint8_t *mem;
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||||
int16_t *qtmem, *vmem;
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||||
int seq_size = qlen + SIMD_WIDTH, ref_size = tlen + SIMD_WIDTH;
|
||||
int i, iStart, D, j, k, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off;
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||||
int Dloop = tlen + qlen; // 循环跳出条件
|
||||
int span, beg1, end1; // 边界条件计算
|
||||
int col_size = qlen + 2 + SIMD_WIDTH;
|
||||
int val_mem_size = (col_size * 9 * 2 + 31) >> 5 << 5; // 32字节的整数倍
|
||||
int mem_size = (seq_size + ref_size) * 2 + val_mem_size;
|
||||
|
||||
SIMD_INIT; // 初始化simd用的数据
|
||||
|
||||
assert(h0 > 0);
|
||||
|
||||
// allocate memory
|
||||
mem = malloc(mem_size);
|
||||
qtmem = (int16_t *)&mem[0];
|
||||
seq = &qtmem[0];
|
||||
ref = &qtmem[seq_size];
|
||||
if (is_left)
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[qlen - 1 - i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i + SIMD_WIDTH] = target[tlen - 1 - i];
|
||||
}
|
||||
else
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i + SIMD_WIDTH] = target[i];
|
||||
}
|
||||
|
||||
vmem = &ref[ref_size];
|
||||
for (i = 0; i < (val_mem_size >> 1); i += SIMD_WIDTH)
|
||||
{
|
||||
_mm256_storeu_si256((__m256i *)&vmem[i], zero_vec);
|
||||
}
|
||||
hA = &vmem[0];
|
||||
mA = &vmem[col_size * 3];
|
||||
eA = &vmem[col_size * 5];
|
||||
fA = &vmem[col_size * 7];
|
||||
|
||||
hA0 = &hA[0];
|
||||
hA1 = &hA[col_size];
|
||||
hA2 = &hA1[col_size];
|
||||
mA1 = &mA[0];
|
||||
mA2 = &mA[col_size];
|
||||
eA1 = &eA[0];
|
||||
eA2 = &eA[col_size];
|
||||
fA1 = &fA[0];
|
||||
fA2 = &fA[col_size];
|
||||
|
||||
// adjust $w if it is too large
|
||||
k = m * m;
|
||||
// get the max score
|
||||
for (i = 0, max = 0; i < k; ++i)
|
||||
max = max > mat[i] ? max : mat[i];
|
||||
max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.);
|
||||
max_ins = max_ins > 1 ? max_ins : 1;
|
||||
w = w < max_ins ? w : max_ins;
|
||||
max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.);
|
||||
max_del = max_del > 1 ? max_del : 1;
|
||||
w = w < max_del ? w : max_del; // TODO: is this necessary?
|
||||
if (tlen < qlen)
|
||||
w = MIN(tlen - 1, w);
|
||||
|
||||
// DP loop
|
||||
max = h0, max_i = max_j = -1;
|
||||
max_ie = -1, gscore = -1;
|
||||
;
|
||||
max_off = 0;
|
||||
beg = 1;
|
||||
end = qlen;
|
||||
// init h0
|
||||
hA0[0] = h0; // 左上角
|
||||
|
||||
if (qlen == 0 || tlen == 0)
|
||||
Dloop = 0; // 防止意外情况
|
||||
if (w >= qlen)
|
||||
{
|
||||
max_ie = 0;
|
||||
gscore = 0;
|
||||
}
|
||||
|
||||
int m_last = 0;
|
||||
int iend;
|
||||
|
||||
for (D = 1; LIKELY(D < Dloop); ++D)
|
||||
{
|
||||
// 边界条件一定要注意！ tlen 大于，等于，小于 qlen时的情况
|
||||
if (D > tlen)
|
||||
{
|
||||
span = MIN(Dloop - D, w);
|
||||
beg1 = MAX(D - tlen + 1, ((D - w) / 2) + 1);
|
||||
}
|
||||
else
|
||||
{
|
||||
span = MIN(D - 1, w);
|
||||
beg1 = MAX(1, ((D - w) / 2) + 1);
|
||||
}
|
||||
end1 = MIN(qlen, beg1 + span);
|
||||
|
||||
if (beg < beg1)
|
||||
beg = beg1;
|
||||
if (end > end1)
|
||||
end = end1;
|
||||
if (beg > end)
|
||||
break; // 不用计算了，直接跳出，否则hA2没有被赋值，里边是上一轮hA0的值，会出bug
|
||||
|
||||
iend = D - (beg - 1); // ref开始计算的位置，倒序
|
||||
span = end - beg;
|
||||
iStart = iend - span - 1; // 0开始的ref索引位置
|
||||
|
||||
// 每一轮需要记录的数据
|
||||
int m = 0, mj = -1, mi = -1;
|
||||
max_vec = zero_vec;
|
||||
|
||||
// 要处理边界
|
||||
// 左边界 处理f (insert)
|
||||
if (iStart == 0)
|
||||
{
|
||||
hA1[end] = MAX(0, h0 - (o_ins + e_ins * end));
|
||||
}
|
||||
// 上边界
|
||||
if (beg == 1)
|
||||
{
|
||||
hA1[0] = MAX(0, h0 - (o_del + e_del * iend));
|
||||
}
|
||||
else
|
||||
{
|
||||
hA1[beg - 1] = 0;
|
||||
eA1[beg - 1] = 0;
|
||||
}
|
||||
|
||||
for (j = beg, i = iend; j <= end + 1 - SIMD_WIDTH; j += SIMD_WIDTH, i -= SIMD_WIDTH)
|
||||
{
|
||||
// 取数据
|
||||
SIMD_LOAD;
|
||||
// 比对seq，计算罚分
|
||||
SIMD_CMP_SEQ;
|
||||
// 计算
|
||||
SIMD_COMPUTE;
|
||||
// 存储结果
|
||||
SIMD_STORE;
|
||||
}
|
||||
// 剩下的计算单元
|
||||
if (j <= end)
|
||||
{
|
||||
// 取数据
|
||||
SIMD_LOAD;
|
||||
// 比对seq，计算罚分
|
||||
SIMD_CMP_SEQ;
|
||||
// 计算
|
||||
SIMD_COMPUTE;
|
||||
// 去除多余计算的部分
|
||||
SIMD_REMOVE_EXTRA;
|
||||
// 存储结果
|
||||
SIMD_STORE;
|
||||
}
|
||||
|
||||
SIMD_FIND_MAX;
|
||||
|
||||
// 注意最后跳出循环j的值
|
||||
j = end + 1;
|
||||
|
||||
if (j == qlen + 1)
|
||||
{
|
||||
max_ie = gscore > hA2[qlen] ? max_ie : iStart;
|
||||
gscore = gscore > hA2[qlen] ? gscore : hA2[qlen];
|
||||
}
|
||||
if (m == 0 && m_last == 0)
|
||||
break; // 一定要注意，斜对角遍历和按列遍历的不同点
|
||||
if (m > max)
|
||||
{
|
||||
max = m, max_i = mi, max_j = mj;
|
||||
max_off = max_off > abs(mj - mi) ? max_off : abs(mj - mi);
|
||||
}
|
||||
else if (zdrop > 0)
|
||||
{
|
||||
if (mi - max_i > mj - max_j)
|
||||
{
|
||||
if (max - m - ((mi - max_i) - (mj - max_j)) * e_del > zdrop)
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (max - m - ((mj - max_j) - (mi - max_i)) * e_ins > zdrop)
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// 调整计算的边界
|
||||
for (j = beg; LIKELY(j <= end); ++j)
|
||||
{
|
||||
int has_val = hA1[j - 1] | hA2[j];
|
||||
if (has_val)
|
||||
break;
|
||||
}
|
||||
beg = j;
|
||||
for (j = end + 1; LIKELY(j >= beg); --j)
|
||||
{
|
||||
int has_val = hA1[j - 1] | hA2[j];
|
||||
if (has_val)
|
||||
break;
|
||||
else
|
||||
hA0[j - 1] = 0;
|
||||
}
|
||||
end = j + 1 <= qlen ? j + 1 : qlen;
|
||||
|
||||
m_last = m;
|
||||
// swap m, h, e, f
|
||||
SWAP_DATA_POINTER;
|
||||
}
|
||||
|
||||
free(mem);
|
||||
if (_qle)
|
||||
*_qle = max_j + 1;
|
||||
if (_tle)
|
||||
*_tle = max_i + 1;
|
||||
if (_gtle)
|
||||
*_gtle = max_ie + 1;
|
||||
if (_gscore)
|
||||
*_gscore = gscore;
|
||||
if (_max_off)
|
||||
*_max_off = max_off;
|
||||
return max;
|
||||
}
|
||||
|
||||
typedef struct
|
||||
{
|
||||
int32_t h, e;
|
||||
} eh_t;
|
||||
|
||||
int ksw_extend2_origin(int qlen, // query length 待匹配段碱基的query长度
|
||||
const uint8_t *query, // read碱基序列
|
||||
int tlen, // target length reference的长度
|
||||
const uint8_t *target, // reference序列
|
||||
int is_left, // 是不是向左扩展
|
||||
int m, // 碱基种类 (5)
|
||||
const int8_t *mat, // 每个位置的query和target的匹配得分 m*m
|
||||
int o_del, // deletion 错配开始的惩罚系数
|
||||
int e_del, // deletion extension的惩罚系数
|
||||
int o_ins, // insertion 错配开始的惩罚系数
|
||||
int e_ins, // insertion extension的惩罚系数
|
||||
int w, // 提前剪枝系数，w =100 匹配位置和beg的最大距离
|
||||
int end_bonus,
|
||||
int zdrop,
|
||||
int h0, // 该seed的初始得分（完全匹配query的碱基数）
|
||||
int *_qle, // 匹配得到全局最大得分的碱基在query的位置
|
||||
int *_tle, // 匹配得到全局最大得分的碱基在reference的位置
|
||||
int *_gtle, // query全部匹配上的target的长度
|
||||
int *_gscore, // query的端到端匹配得分
|
||||
int *_max_off) // 取得最大得分时在query和reference上位置差的 最大值
|
||||
{
|
||||
eh_t *eh; // score array
|
||||
int8_t *qp; // query profile
|
||||
int i, j, k, oe_del = o_del + e_del, oe_ins = o_ins + e_ins, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off;
|
||||
uint8_t *qmem, *ref, *seq;
|
||||
assert(h0 > 0);
|
||||
// allocate memory
|
||||
qp = malloc(qlen * m);
|
||||
eh = calloc(qlen + 1, 8);
|
||||
qmem = malloc(qlen + tlen);
|
||||
seq = (uint8_t *)&qmem[0];
|
||||
ref = (uint8_t *)&qmem[qlen];
|
||||
if (is_left)
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[qlen - 1 - i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i] = target[tlen - 1 - i];
|
||||
}
|
||||
else
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i] = target[i];
|
||||
}
|
||||
// generate the query profile
|
||||
for (k = i = 0; k < m; ++k)
|
||||
{
|
||||
const int8_t *p = &mat[k * m];
|
||||
for (j = 0; j < qlen; ++j)
|
||||
qp[i++] = p[seq[j]];
|
||||
}
|
||||
// fill the first row
|
||||
eh[0].h = h0;
|
||||
eh[1].h = h0 > oe_ins ? h0 - oe_ins : 0;
|
||||
for (j = 2; j <= qlen && eh[j - 1].h > e_ins; ++j)
|
||||
eh[j].h = eh[j - 1].h - e_ins;
|
||||
// adjust $w if it is too large
|
||||
k = m * m;
|
||||
for (i = 0, max = 0; i < k; ++i) // get the max score
|
||||
max = max > mat[i] ? max : mat[i];
|
||||
max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.);
|
||||
max_ins = max_ins > 1 ? max_ins : 1;
|
||||
w = w < max_ins ? w : max_ins;
|
||||
max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.);
|
||||
max_del = max_del > 1 ? max_del : 1;
|
||||
w = w < max_del ? w : max_del; // TODO: is this necessary?
|
||||
// printf("%d\n", w);
|
||||
// DP loop
|
||||
max = h0, max_i = max_j = -1;
|
||||
max_ie = -1, gscore = -1;
|
||||
max_off = 0;
|
||||
beg = 0, end = qlen;
|
||||
|
||||
for (i = 0; LIKELY(i < tlen); ++i)
|
||||
{
|
||||
int t, f = 0, h1, m = 0, mj = -1;
|
||||
int8_t *q = &qp[ref[i] * qlen];
|
||||
// apply the band and the constraint (if provided)
|
||||
if (beg < i - w)
|
||||
beg = i - w;
|
||||
if (end > i + w + 1)
|
||||
end = i + w + 1;
|
||||
// if (end > qlen) end = qlen; 没用
|
||||
// compute the first column
|
||||
if (beg == 0)
|
||||
{
|
||||
h1 = h0 - (o_del + e_del * (i + 1));
|
||||
if (h1 < 0)
|
||||
h1 = 0;
|
||||
}
|
||||
else
|
||||
h1 = 0;
|
||||
for (j = beg; LIKELY(j < end); ++j)
|
||||
{
|
||||
// At the beginning of the loop: eh[j] = { H(i-1,j-1), E(i,j) }, f = F(i,j) and h1 = H(i,j-1)
|
||||
// Similar to SSE2-SW, cells are computed in the following order:
|
||||
// H(i,j) = max{H(i-1,j-1)+S(i,j), E(i,j), F(i,j)}
|
||||
// E(i+1,j) = max{H(i,j)-gapo, E(i,j)} - gape
|
||||
// F(i,j+1) = max{H(i,j)-gapo, F(i,j)} - gape
|
||||
eh_t *p = &eh[j];
|
||||
int h, M = p->h, e = p->e; // get H(i-1,j-1) and E(i-1,j)
|
||||
p->h = h1; // set H(i,j-1) for the next row
|
||||
M = M ? M + q[j] : 0; // separating H and M to disallow a cigar like "100M3I3D20M"
|
||||
h = M > e ? M : e; // e and f are guaranteed to be non-negative, so h>=0 even if M<0
|
||||
h = h > f ? h : f;
|
||||
h1 = h; // save H(i,j) to h1 for the next column
|
||||
mj = m > h ? mj : j; // record the position where max score is achieved
|
||||
m = m > h ? m : h; // m is stored at eh[mj+1]
|
||||
t = M - oe_del;
|
||||
t = t > 0 ? t : 0;
|
||||
e -= e_del;
|
||||
e = e > t ? e : t; // computed E(i+1,j)
|
||||
p->e = e; // save E(i+1,j) for the next row
|
||||
t = M - oe_ins;
|
||||
t = t > 0 ? t : 0;
|
||||
f -= e_ins;
|
||||
f = f > t ? f : t; // computed F(i,j+1)
|
||||
}
|
||||
eh[end].h = h1;
|
||||
eh[end].e = 0;
|
||||
if (j == qlen)
|
||||
{
|
||||
max_ie = gscore > h1 ? max_ie : i;
|
||||
gscore = gscore > h1 ? gscore : h1;
|
||||
}
|
||||
if (m == 0)
|
||||
break;
|
||||
if (m > max)
|
||||
{
|
||||
max = m, max_i = i, max_j = mj;
|
||||
max_off = max_off > abs(mj - i) ? max_off : abs(mj - i);
|
||||
}
|
||||
else if (zdrop > 0)
|
||||
{
|
||||
if (i - max_i > mj - max_j)
|
||||
{
|
||||
if (max - m - ((i - max_i) - (mj - max_j)) * e_del > zdrop)
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (max - m - ((mj - max_j) - (i - max_i)) * e_ins > zdrop)
|
||||
break;
|
||||
}
|
||||
}
|
||||
// update beg and end for the next round
|
||||
for (j = beg; LIKELY(j < end) && eh[j].h == 0 && eh[j].e == 0; ++j)
|
||||
;
|
||||
beg = j;
|
||||
for (j = end; LIKELY(j >= beg) && eh[j].h == 0 && eh[j].e == 0; --j)
|
||||
;
|
||||
end = j + 2 < qlen ? j + 2 : qlen;
|
||||
// beg = 0; end = qlen; // uncomment this line for debugging
|
||||
}
|
||||
|
||||
free(eh);
|
||||
free(qp);
|
||||
free(qmem);
|
||||
if (_qle)
|
||||
*_qle = max_j + 1;
|
||||
if (_tle)
|
||||
*_tle = max_i + 1;
|
||||
if (_gtle)
|
||||
*_gtle = max_ie + 1;
|
||||
if (_gscore)
|
||||
*_gscore = gscore;
|
||||
if (_max_off)
|
||||
*_max_off = max_off;
|
||||
return max;
|
||||
}
|
||||
|
|
@ -0,0 +1,450 @@
|
|||
#include <stdlib.h>
|
||||
#include <stdint.h>
|
||||
#include <assert.h>
|
||||
#include <emmintrin.h>
|
||||
#include <stdio.h>
|
||||
#include <immintrin.h>
|
||||
#include <emmintrin.h>
|
||||
|
||||
#ifdef __GNUC__
|
||||
#define LIKELY(x) __builtin_expect((x), 1)
|
||||
#define UNLIKELY(x) __builtin_expect((x), 0)
|
||||
#else
|
||||
#define LIKELY(x) (x)
|
||||
#define UNLIKELY(x) (x)
|
||||
#endif
|
||||
|
||||
#undef MAX
|
||||
#undef MIN
|
||||
#define MAX(x, y) ((x) > (y) ? (x) : (y))
|
||||
#define MIN(x, y) ((x) < (y) ? (x) : (y))
|
||||
#define SIMD_WIDTH 32
|
||||
|
||||
static const uint8_t h_vec_int_mask[SIMD_WIDTH][SIMD_WIDTH] = {
|
||||
{0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0},
|
||||
{0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}};
|
||||
|
||||
// static const uint8_t reverse_mask[SIMD_WIDTH] = {1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 15, 14, 1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 15, 14};
|
||||
static const uint8_t reverse_mask[SIMD_WIDTH] = {7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8};
|
||||
|
||||
// const int permute_mask = _MM_SHUFFLE(0, 1, 2, 3);
|
||||
#define permute_mask _MM_SHUFFLE(0, 1, 2, 3)
|
||||
// 初始化变量
|
||||
#define SIMD_INIT \
|
||||
int oe_del = o_del + e_del, oe_ins = o_ins + e_ins; \
|
||||
__m256i zero_vec; \
|
||||
__m256i max_vec; \
|
||||
__m256i oe_del_vec; \
|
||||
__m256i oe_ins_vec; \
|
||||
__m256i e_del_vec; \
|
||||
__m256i e_ins_vec; \
|
||||
__m256i h_vec_mask[SIMD_WIDTH]; \
|
||||
__m256i reverse_mask_vec; \
|
||||
zero_vec = _mm256_setzero_si256(); \
|
||||
oe_del_vec = _mm256_set1_epi8(oe_del); \
|
||||
oe_ins_vec = _mm256_set1_epi8(oe_ins); \
|
||||
e_del_vec = _mm256_set1_epi8(e_del); \
|
||||
e_ins_vec = _mm256_set1_epi8(e_ins); \
|
||||
__m256i match_sc_vec = _mm256_set1_epi8(a); \
|
||||
__m256i mis_sc_vec = _mm256_set1_epi8(b); \
|
||||
__m256i amb_sc_vec = _mm256_set1_epi8(1); \
|
||||
__m256i amb_vec = _mm256_set1_epi8(4); \
|
||||
reverse_mask_vec = _mm256_loadu_si256((__m256i *)(reverse_mask)); \
|
||||
for (i = 0; i < SIMD_WIDTH; ++i) \
|
||||
h_vec_mask[i] = _mm256_loadu_si256((__m256i *)(&h_vec_int_mask[i]));
|
||||
|
||||
/*
|
||||
* e 表示当前ref的碱基被删除
|
||||
* f 表示当前seq的碱基插入
|
||||
* m 表示当前碱基匹配（可以相等，也可以不想等）
|
||||
* h 表示最大值
|
||||
*/
|
||||
// load向量化数据
|
||||
#define SIMD_LOAD \
|
||||
__m256i m1 = _mm256_loadu_si256((__m256i *)(&mA1[j])); \
|
||||
__m256i e1 = _mm256_loadu_si256((__m256i *)(&eA1[j])); \
|
||||
__m256i m1j1 = _mm256_loadu_si256((__m256i *)(&mA1[j - 1])); \
|
||||
__m256i f1j1 = _mm256_loadu_si256((__m256i *)(&fA1[j - 1])); \
|
||||
__m256i h0j1 = _mm256_loadu_si256((__m256i *)(&hA0[j - 1])); \
|
||||
__m256i qs_vec = _mm256_loadu_si256((__m256i *)(&seq[j - 1])); \
|
||||
__m256i ts_vec = _mm256_loadu_si256((__m256i *)(&ref[i]));
|
||||
|
||||
// 比对ref和seq的序列，计算罚分
|
||||
#define SIMD_CMP_SEQ \
|
||||
ts_vec = _mm256_permute4x64_epi64(ts_vec, permute_mask); \
|
||||
ts_vec = _mm256_shuffle_epi8(ts_vec, reverse_mask_vec); \
|
||||
__m256i match_mask_vec = _mm256_cmpeq_epi8(qs_vec, ts_vec); \
|
||||
__m256i mis_score_vec = _mm256_andnot_si256(match_mask_vec, mis_sc_vec); \
|
||||
__m256i match_score_vec = _mm256_and_si256(match_sc_vec, match_mask_vec); \
|
||||
__m256i q_amb_mask_vec = _mm256_cmpeq_epi8(qs_vec, amb_vec); \
|
||||
__m256i t_amb_mask_vec = _mm256_cmpeq_epi8(ts_vec, amb_vec); \
|
||||
__m256i amb_mask_vec = _mm256_or_si256(q_amb_mask_vec, t_amb_mask_vec); \
|
||||
__m256i amb_score_vec = _mm256_and_si256(amb_mask_vec, amb_sc_vec); \
|
||||
mis_score_vec = _mm256_andnot_si256(amb_mask_vec, mis_score_vec); \
|
||||
mis_score_vec = _mm256_or_si256(amb_score_vec, mis_score_vec); \
|
||||
match_score_vec = _mm256_andnot_si256(amb_mask_vec, match_score_vec);
|
||||
|
||||
// 向量化计算h, e, f, m
|
||||
#define SIMD_COMPUTE \
|
||||
__m256i en_vec0 = _mm256_max_epu8(m1, oe_del_vec); \
|
||||
en_vec0 = _mm256_subs_epu8(en_vec0, oe_del_vec); \
|
||||
__m256i en_vec1 = _mm256_max_epu8(e1, e_del_vec); \
|
||||
en_vec1 = _mm256_subs_epu8(en_vec1, e_del_vec); \
|
||||
__m256i en_vec = _mm256_max_epu8(en_vec0, en_vec1); \
|
||||
__m256i fn_vec0 = _mm256_max_epu8(m1j1, oe_ins_vec); \
|
||||
fn_vec0 = _mm256_subs_epu8(fn_vec0, oe_ins_vec); \
|
||||
__m256i fn_vec1 = _mm256_max_epu8(f1j1, e_ins_vec); \
|
||||
fn_vec1 = _mm256_subs_epu8(fn_vec1, e_ins_vec); \
|
||||
__m256i fn_vec = _mm256_max_epu8(fn_vec0, fn_vec1); \
|
||||
__m256i mn_vec0 = _mm256_adds_epu8(h0j1, match_score_vec); \
|
||||
mn_vec0 = _mm256_max_epu8(mn_vec0, mis_score_vec); \
|
||||
mn_vec0 = _mm256_subs_epu8(mn_vec0, mis_score_vec); \
|
||||
__m256i mn_mask = _mm256_cmpeq_epi8(h0j1, zero_vec); \
|
||||
__m256i mn_vec = _mm256_andnot_si256(mn_mask, mn_vec0); \
|
||||
__m256i hn_vec0 = _mm256_max_epu8(en_vec, fn_vec); \
|
||||
__m256i hn_vec = _mm256_max_epu8(hn_vec0, mn_vec);
|
||||
|
||||
// 存储向量化结果
|
||||
#define SIMD_STORE \
|
||||
max_vec = _mm256_max_epu8(max_vec, hn_vec); \
|
||||
_mm256_storeu_si256((__m256i *)&eA2[j], en_vec); \
|
||||
_mm256_storeu_si256((__m256i *)&fA2[j], fn_vec); \
|
||||
_mm256_storeu_si256((__m256i *)&mA2[j], mn_vec); \
|
||||
_mm256_storeu_si256((__m256i *)&hA2[j], hn_vec);
|
||||
|
||||
// 去除多余的部分
|
||||
#define SIMD_REMOVE_EXTRA \
|
||||
en_vec = _mm256_and_si256(en_vec, h_vec_mask[end - j]); \
|
||||
fn_vec = _mm256_and_si256(fn_vec, h_vec_mask[end - j]); \
|
||||
mn_vec = _mm256_and_si256(mn_vec, h_vec_mask[end - j]); \
|
||||
hn_vec = _mm256_and_si256(hn_vec, h_vec_mask[end - j]);
|
||||
|
||||
// 找最大值和位置
|
||||
#define SIMD_FIND_MAX \
|
||||
uint8_t *maxVal = (uint8_t *)&max_vec; \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 1)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 2)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 3)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 4)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 5)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 6)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 7)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_alignr_epi8(max_vec, max_vec, 8)); \
|
||||
max_vec = _mm256_max_epu8(max_vec, _mm256_permute2x128_si256(max_vec, max_vec, 0x01)); \
|
||||
m = maxVal[0]; \
|
||||
if (m > 0) \
|
||||
{ \
|
||||
for (j = beg, i = iend; j <= end; j += SIMD_WIDTH, i -= SIMD_WIDTH) \
|
||||
{ \
|
||||
__m256i h2_vec = _mm256_loadu_si256((__m256i *)(&hA2[j])); \
|
||||
__m256i vcmp = _mm256_cmpeq_epi8(h2_vec, max_vec); \
|
||||
uint32_t mask = _mm256_movemask_epi8(vcmp); \
|
||||
if (mask > 0) \
|
||||
{ \
|
||||
int pos = SIMD_WIDTH - 1 - __builtin_clz(mask); \
|
||||
mj = j - 1 + pos; \
|
||||
mi = i - 1 - pos; \
|
||||
} \
|
||||
} \
|
||||
}
|
||||
|
||||
// 每轮迭代后，交换数组
|
||||
#define SWAP_DATA_POINTER \
|
||||
uint8_t *tmp = hA0; \
|
||||
hA0 = hA1; \
|
||||
hA1 = hA2; \
|
||||
hA2 = tmp; \
|
||||
tmp = eA1; \
|
||||
eA1 = eA2; \
|
||||
eA2 = tmp; \
|
||||
tmp = fA1; \
|
||||
fA1 = fA2; \
|
||||
fA2 = tmp; \
|
||||
tmp = mA1; \
|
||||
mA1 = mA2; \
|
||||
mA2 = tmp;
|
||||
|
||||
int ksw_avx2_u8(int qlen, // query length 待匹配段碱基的query长度
|
||||
const uint8_t *query, // read碱基序列
|
||||
int tlen, // target length reference的长度
|
||||
const uint8_t *target, // reference序列
|
||||
int is_left, // 是不是向左扩展
|
||||
int m, // 碱基种类 (5)
|
||||
const int8_t *mat, // 每个位置的query和target的匹配得分 m*m
|
||||
int o_del, // deletion 错配开始的惩罚系数
|
||||
int e_del, // deletion extension的惩罚系数
|
||||
int o_ins, // insertion 错配开始的惩罚系数
|
||||
int e_ins, // insertion extension的惩罚系数
|
||||
int a, // 碱基match时的分数
|
||||
int b, // 碱基mismatch时的惩罚分数（正数）
|
||||
int w, // 提前剪枝系数，w =100 匹配位置和beg的最大距离
|
||||
int end_bonus,
|
||||
int zdrop,
|
||||
int h0, // 该seed的初始得分（完全匹配query的碱基数）
|
||||
int *_qle, // 匹配得到全局最大得分的碱基在query的位置
|
||||
int *_tle, // 匹配得到全局最大得分的碱基在reference的位置
|
||||
int *_gtle, // query全部匹配上的target的长度
|
||||
int *_gscore, // query的端到端匹配得分
|
||||
int *_max_off) // 取得最大得分时在query和reference上位置差的 最大值
|
||||
{
|
||||
uint8_t *mA, *hA, *eA, *fA, *mA1, *mA2, *hA0, *hA1, *eA1, *fA1, *hA2, *eA2, *fA2; // hA0保存上上个col的H，其他的保存上个H E F M
|
||||
uint8_t *seq, *ref;
|
||||
uint8_t *mem, *qtmem, *vmem;
|
||||
int seq_size = qlen + SIMD_WIDTH, ref_size = tlen + SIMD_WIDTH;
|
||||
int i, iStart, D, j, k, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off;
|
||||
int Dloop = tlen + qlen; // 循环跳出条件
|
||||
int span, beg1, end1; // 边界条件计算
|
||||
int col_size = qlen + 2 + SIMD_WIDTH;
|
||||
int val_mem_size = (col_size * 9 + 31) >> 5 << 5; // 32字节的整数倍
|
||||
int mem_size = seq_size + ref_size + val_mem_size;
|
||||
|
||||
SIMD_INIT; // 初始化simd用的数据
|
||||
|
||||
assert(h0 > 0);
|
||||
|
||||
// allocate memory
|
||||
mem = malloc(mem_size);
|
||||
qtmem = &mem[0];
|
||||
seq = (uint8_t *)&qtmem[0];
|
||||
ref = (uint8_t *)&qtmem[seq_size];
|
||||
if (is_left)
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[qlen - 1 - i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i + SIMD_WIDTH] = target[tlen - 1 - i];
|
||||
}
|
||||
else
|
||||
{
|
||||
for (i = 0; i < qlen; ++i)
|
||||
seq[i] = query[i];
|
||||
for (i = 0; i < tlen; ++i)
|
||||
ref[i + SIMD_WIDTH] = target[i];
|
||||
}
|
||||
|
||||
vmem = &ref[ref_size];
|
||||
for (i = 0; i < val_mem_size; i += SIMD_WIDTH)
|
||||
{
|
||||
_mm256_storeu_si256((__m256i *)&vmem[i], zero_vec);
|
||||
}
|
||||
|
||||
hA = &vmem[0];
|
||||
mA = &vmem[col_size * 3];
|
||||
eA = &vmem[col_size * 5];
|
||||
fA = &vmem[col_size * 7];
|
||||
|
||||
hA0 = &hA[0];
|
||||
hA1 = &hA[col_size];
|
||||
hA2 = &hA1[col_size];
|
||||
mA1 = &mA[0];
|
||||
mA2 = &mA[col_size];
|
||||
eA1 = &eA[0];
|
||||
eA2 = &eA[col_size];
|
||||
fA1 = &fA[0];
|
||||
fA2 = &fA[col_size];
|
||||
|
||||
// adjust $w if it is too large
|
||||
k = m * m;
|
||||
// get the max score
|
||||
for (i = 0, max = 0; i < k; ++i)
|
||||
max = max > mat[i] ? max : mat[i];
|
||||
max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.);
|
||||
max_ins = max_ins > 1 ? max_ins : 1;
|
||||
w = w < max_ins ? w : max_ins;
|
||||
max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.);
|
||||
max_del = max_del > 1 ? max_del : 1;
|
||||
w = w < max_del ? w : max_del; // TODO: is this necessary?
|
||||
if (tlen < qlen)
|
||||
w = MIN(tlen - 1, w);
|
||||
|
||||
// DP loop
|
||||
max = h0, max_i = max_j = -1;
|
||||
max_ie = -1, gscore = -1;
|
||||
;
|
||||
max_off = 0;
|
||||
beg = 1;
|
||||
end = qlen;
|
||||
// init h0
|
||||
hA0[0] = h0; // 左上角
|
||||
|
||||
if (qlen == 0 || tlen == 0)
|
||||
Dloop = 0; // 防止意外情况
|
||||
if (w >= qlen)
|
||||
{
|
||||
max_ie = 0;
|
||||
gscore = 0;
|
||||
}
|
||||
|
||||
int m_last = 0;
|
||||
int iend;
|
||||
|
||||
for (D = 1; LIKELY(D < Dloop); ++D)
|
||||
{
|
||||
// 边界条件一定要注意！ tlen 大于，等于，小于 qlen时的情况
|
||||
if (D > tlen)
|
||||
{
|
||||
span = MIN(Dloop - D, w);
|
||||
beg1 = MAX(D - tlen + 1, ((D - w) / 2) + 1);
|
||||
}
|
||||
else
|
||||
{
|
||||
span = MIN(D - 1, w);
|
||||
beg1 = MAX(1, ((D - w) / 2) + 1);
|
||||
}
|
||||
end1 = MIN(qlen, beg1 + span);
|
||||
|
||||
if (beg < beg1)
|
||||
beg = beg1;
|
||||
if (end > end1)
|
||||
end = end1;
|
||||
if (beg > end)
|
||||
break; // 不用计算了，直接跳出，否则hA2没有被赋值，里边是上一轮hA0的值，会出bug
|
||||
|
||||
iend = D - (beg - 1); // ref开始计算的位置，倒序
|
||||
span = end - beg;
|
||||
iStart = iend - span - 1; // 0开始的ref索引位置
|
||||
|
||||
// 每一轮需要记录的数据
|
||||
int m = 0, mj = -1, mi = -1;
|
||||
max_vec = zero_vec;
|
||||
|
||||
// 要处理边界
|
||||
// 左边界 处理f (insert)
|
||||
if (iStart == 0)
|
||||
{
|
||||
hA1[end] = MAX(0, h0 - (o_ins + e_ins * end));
|
||||
}
|
||||
// 上边界
|
||||
if (beg == 1)
|
||||
{
|
||||
hA1[0] = MAX(0, h0 - (o_del + e_del * iend));
|
||||
}
|
||||
else
|
||||
{
|
||||
hA1[beg - 1] = 0;
|
||||
eA1[beg - 1] = 0;
|
||||
}
|
||||
|
||||
for (j = beg, i = iend; j <= end + 1 - SIMD_WIDTH; j += SIMD_WIDTH, i -= SIMD_WIDTH)
|
||||
{
|
||||
// 取数据
|
||||
SIMD_LOAD;
|
||||
// 比对seq，计算罚分
|
||||
SIMD_CMP_SEQ;
|
||||
// 计算
|
||||
SIMD_COMPUTE;
|
||||
// 存储结果
|
||||
SIMD_STORE;
|
||||
}
|
||||
// 剩下的计算单元
|
||||
if (j <= end)
|
||||
{
|
||||
// 取数据
|
||||
SIMD_LOAD;
|
||||
// 比对seq，计算罚分
|
||||
SIMD_CMP_SEQ;
|
||||
// 计算
|
||||
SIMD_COMPUTE;
|
||||
// 去除多余计算的部分
|
||||
SIMD_REMOVE_EXTRA;
|
||||
// 存储结果
|
||||
SIMD_STORE;
|
||||
}
|
||||
|
||||
SIMD_FIND_MAX;
|
||||
|
||||
// 注意最后跳出循环j的值
|
||||
j = end + 1;
|
||||
|
||||
if (j == qlen + 1)
|
||||
{
|
||||
max_ie = gscore > hA2[qlen] ? max_ie : iStart;
|
||||
gscore = gscore > hA2[qlen] ? gscore : hA2[qlen];
|
||||
}
|
||||
if (m == 0 && m_last == 0)
|
||||
break; // 一定要注意，斜对角遍历和按列遍历的不同点
|
||||
if (m > max)
|
||||
{
|
||||
max = m, max_i = mi, max_j = mj;
|
||||
max_off = max_off > abs(mj - mi) ? max_off : abs(mj - mi);
|
||||
}
|
||||
else if (zdrop > 0)
|
||||
{
|
||||
if (mi - max_i > mj - max_j)
|
||||
{
|
||||
if (max - m - ((mi - max_i) - (mj - max_j)) * e_del > zdrop)
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (max - m - ((mj - max_j) - (mi - max_i)) * e_ins > zdrop)
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// 调整计算的边界
|
||||
for (j = beg; LIKELY(j <= end); ++j)
|
||||
{
|
||||
int has_val = hA1[j - 1] | hA2[j];
|
||||
if (has_val)
|
||||
break;
|
||||
}
|
||||
beg = j;
|
||||
for (j = end + 1; LIKELY(j >= beg); --j)
|
||||
{
|
||||
int has_val = hA1[j - 1] | hA2[j];
|
||||
if (has_val)
|
||||
break;
|
||||
else
|
||||
hA0[j - 1] = 0;
|
||||
}
|
||||
end = j + 1 <= qlen ? j + 1 : qlen;
|
||||
|
||||
m_last = m;
|
||||
// swap m, h, e, f
|
||||
SWAP_DATA_POINTER;
|
||||
}
|
||||
|
||||
free(mem);
|
||||
if (_qle)
|
||||
*_qle = max_j + 1;
|
||||
if (_tle)
|
||||
*_tle = max_i + 1;
|
||||
if (_gtle)
|
||||
*_gtle = max_ie + 1;
|
||||
if (_gscore)
|
||||
*_gscore = gscore;
|
||||
if (_max_off)
|
||||
*_max_off = max_off;
|
||||
return max;
|
||||
}
|
||||
|
|
@ -0,0 +1,148 @@
|
|||
#include <stdint.h>
|
||||
#include <stdlib.h>
|
||||
#include <assert.h>
|
||||
|
||||
#ifdef __GNUC__
|
||||
#define LIKELY(x) __builtin_expect((x), 1)
|
||||
#define UNLIKELY(x) __builtin_expect((x), 0)
|
||||
#else
|
||||
#define LIKELY(x) (x)
|
||||
#define UNLIKELY(x) (x)
|
||||
#endif
|
||||
|
||||
typedef struct
|
||||
{
|
||||
int32_t h, e;
|
||||
} eh_t;
|
||||
|
||||
int ksw_normal(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int m, const int8_t *mat, int o_del, int e_del, int o_ins, int e_ins, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off)
|
||||
{
|
||||
eh_t *eh; // score array
|
||||
int8_t *qp; // query profile
|
||||
int i, j, k, oe_del = o_del + e_del, oe_ins = o_ins + e_ins, beg, end, max, max_i, max_j, max_ins, max_del, max_ie, gscore, max_off;
|
||||
assert(h0 > 0);
|
||||
qp = malloc(qlen * m);
|
||||
eh = calloc(qlen + 1, 8);
|
||||
// generate the query profile
|
||||
for (k = i = 0; k < m; ++k)
|
||||
{
|
||||
const int8_t *p = &mat[k * m];
|
||||
for (j = 0; j < qlen; ++j)
|
||||
qp[i++] = p[query[j]];
|
||||
}
|
||||
// fill the first row
|
||||
eh[0].h = h0;
|
||||
eh[1].h = h0 > oe_ins ? h0 - oe_ins : 0;
|
||||
for (j = 2; j <= qlen && eh[j - 1].h > e_ins; ++j)
|
||||
eh[j].h = eh[j - 1].h - e_ins;
|
||||
// adjust $w if it is too large
|
||||
k = m * m;
|
||||
for (i = 0, max = 0; i < k; ++i) // get the max score
|
||||
max = max > mat[i] ? max : mat[i];
|
||||
max_ins = (int)((double)(qlen * max + end_bonus - o_ins) / e_ins + 1.);
|
||||
max_ins = max_ins > 1 ? max_ins : 1;
|
||||
w = w < max_ins ? w : max_ins;
|
||||
max_del = (int)((double)(qlen * max + end_bonus - o_del) / e_del + 1.);
|
||||
max_del = max_del > 1 ? max_del : 1;
|
||||
w = w < max_del ? w : max_del; // TODO: is this necessary?
|
||||
// DP loop
|
||||
max = h0, max_i = max_j = -1;
|
||||
max_ie = -1, gscore = -1;
|
||||
max_off = 0;
|
||||
beg = 0, end = qlen;
|
||||
for (i = 0; LIKELY(i < tlen); ++i)
|
||||
{
|
||||
int t, f = 0, h1, m = 0, mj = -1;
|
||||
int8_t *q = &qp[target[i] * qlen];
|
||||
// apply the band and the constraint (if provided)
|
||||
if (beg < i - w)
|
||||
beg = i - w;
|
||||
if (end > i + w + 1)
|
||||
end = i + w + 1;
|
||||
if (end > qlen)
|
||||
end = qlen;
|
||||
// compute the first column
|
||||
if (beg == 0)
|
||||
{
|
||||
h1 = h0 - (o_del + e_del * (i + 1));
|
||||
if (h1 < 0)
|
||||
h1 = 0;
|
||||
}
|
||||
else
|
||||
h1 = 0;
|
||||
for (j = beg; LIKELY(j < end); ++j)
|
||||
{
|
||||
// At the beginning of the loop: eh[j] = { H(i-1,j-1), E(i,j) }, f = F(i,j) and h1 = H(i,j-1)
|
||||
// Similar to SSE2-SW, cells are computed in the following order:
|
||||
// H(i,j) = max{H(i-1,j-1)+S(i,j), E(i,j), F(i,j)}
|
||||
// E(i+1,j) = max{H(i,j)-gapo, E(i,j)} - gape
|
||||
// F(i,j+1) = max{H(i,j)-gapo, F(i,j)} - gape
|
||||
eh_t *p = &eh[j];
|
||||
int h, M = p->h, e = p->e; // get H(i-1,j-1) and E(i-1,j)
|
||||
p->h = h1; // set H(i,j-1) for the next row
|
||||
M = M ? M + q[j] : 0; // separating H and M to disallow a cigar like "100M3I3D20M"
|
||||
h = M > e ? M : e; // e and f are guaranteed to be non-negative, so h>=0 even if M<0
|
||||
h = h > f ? h : f;
|
||||
h1 = h; // save H(i,j) to h1 for the next column
|
||||
mj = m > h ? mj : j; // record the position where max score is achieved
|
||||
m = m > h ? m : h; // m is stored at eh[mj+1]
|
||||
t = M - oe_del;
|
||||
t = t > 0 ? t : 0;
|
||||
e -= e_del;
|
||||
e = e > t ? e : t; // computed E(i+1,j)
|
||||
p->e = e; // save E(i+1,j) for the next row
|
||||
t = M - oe_ins;
|
||||
t = t > 0 ? t : 0;
|
||||
f -= e_ins;
|
||||
f = f > t ? f : t; // computed F(i,j+1)
|
||||
}
|
||||
eh[end].h = h1;
|
||||
eh[end].e = 0;
|
||||
if (j == qlen)
|
||||
{
|
||||
max_ie = gscore > h1 ? max_ie : i;
|
||||
gscore = gscore > h1 ? gscore : h1;
|
||||
}
|
||||
if (m == 0)
|
||||
break;
|
||||
if (m > max)
|
||||
{
|
||||
max = m, max_i = i, max_j = mj;
|
||||
max_off = max_off > abs(mj - i) ? max_off : abs(mj - i);
|
||||
}
|
||||
else if (zdrop > 0)
|
||||
{
|
||||
if (i - max_i > mj - max_j)
|
||||
{
|
||||
if (max - m - ((i - max_i) - (mj - max_j)) * e_del > zdrop)
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (max - m - ((mj - max_j) - (i - max_i)) * e_ins > zdrop)
|
||||
break;
|
||||
}
|
||||
}
|
||||
// update beg and end for the next round
|
||||
for (j = beg; LIKELY(j < end) && eh[j].h == 0 && eh[j].e == 0; ++j)
|
||||
;
|
||||
beg = j;
|
||||
for (j = end; LIKELY(j >= beg) && eh[j].h == 0 && eh[j].e == 0; --j)
|
||||
;
|
||||
end = j + 2 < qlen ? j + 2 : qlen;
|
||||
// beg = 0; end = qlen; // uncomment this line for debugging
|
||||
}
|
||||
free(eh);
|
||||
free(qp);
|
||||
if (_qle)
|
||||
*_qle = max_j + 1;
|
||||
if (_tle)
|
||||
*_tle = max_i + 1;
|
||||
if (_gtle)
|
||||
*_gtle = max_ie + 1;
|
||||
if (_gscore)
|
||||
*_gscore = gscore;
|
||||
if (_max_off)
|
||||
*_max_off = max_off;
|
||||
return max;
|
||||
}
|
||||
|
|
@ -0,0 +1,262 @@
|
|||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
#include <stdint.h>
|
||||
#include <assert.h>
|
||||
#include "sys/time.h"
|
||||
|
||||
#define SW_NORMAL 0
|
||||
#define SW_AVX2 1
|
||||
#define SW_CUDA 2
|
||||
#define SW_ALL 3
|
||||
|
||||
#define BLOCK_BUF_SIZE 1048576
|
||||
#define READ_BUF_SIZE 2048
|
||||
#define SEQ_BUF_SIZE (BLOCK_BUF_SIZE + READ_BUF_SIZE)
|
||||
|
||||
#ifdef SHOW_PERF
|
||||
// 用来调试，计算感兴趣部分的运行时间
|
||||
// 获取当前毫秒数
|
||||
int64_t get_mseconds()
|
||||
{
|
||||
struct timeval tv;
|
||||
gettimeofday(&tv, NULL);
|
||||
return (int64_t)1000 * (tv.tv_sec + ((1e-6) * tv.tv_usec));
|
||||
}
|
||||
|
||||
int64_t time_sw_normal = 0,
|
||||
time_sw_avx2 = 0,
|
||||
time_sw_avx2_u8 = 0;
|
||||
|
||||
#endif
|
||||
|
||||
extern int ksw_normal(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int m, const int8_t *mat, int o_del, int e_del, int o_ins, int e_ins, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off);
|
||||
extern int ksw_avx2(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int is_left, int m, const int8_t *mat, int o_del, int e_del,
|
||||
int o_ins, int e_ins, int a, int b, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off);
|
||||
extern int ksw_avx2_u8(int qlen, const uint8_t *query, int tlen, const uint8_t *target, int is_left, int m, const int8_t *mat, int o_del, int e_del,
|
||||
int o_ins, int e_ins, int a, int b, int w, int end_bonus, int zdrop, int h0, int *_qle, int *_tle, int *_gtle, int *_gscore, int *_max_off);
|
||||
|
||||
/*
|
||||
* 包含一个参数，用来区分调用那个sw算法
|
||||
* 参数为 normal/avx2/cuda
|
||||
*/
|
||||
// 程序执行入口
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
/*
|
||||
int sw_algo = SW_NORMAL;
|
||||
|
||||
// 判断执行的sw的实现类型
|
||||
if (argc > 1)
|
||||
{
|
||||
if (strcmp(argv[1], "normal") == 0)
|
||||
{
|
||||
sw_algo = SW_NORMAL;
|
||||
}
|
||||
else if (strcmp(argv[1], "avx2") == 0)
|
||||
{
|
||||
sw_algo = SW_AVX2;
|
||||
}
|
||||
else if (strcmp(argv[1], "cuda") == 0)
|
||||
{
|
||||
sw_algo = SW_CUDA;
|
||||
}
|
||||
else
|
||||
{
|
||||
sw_algo = SW_ALL;
|
||||
}
|
||||
} */
|
||||
|
||||
// 初始化一些全局参数
|
||||
int8_t mat[25] = {1, -4, -4, -4, -1,
|
||||
-4, 1, -4, -4, -1,
|
||||
-4, -4, 1, -4, -1,
|
||||
-4, -4, -4, 1, -1,
|
||||
-1, -1, -1, -1, -1};
|
||||
int max_off[2];
|
||||
int qle, tle, gtle, gscore;
|
||||
|
||||
// 读取测试数据
|
||||
char *query_arr = (char *)malloc(SEQ_BUF_SIZE);
|
||||
char *target_arr = (char *)malloc(SEQ_BUF_SIZE);
|
||||
int *info_buf = (int *)malloc(SEQ_BUF_SIZE);
|
||||
int **info_arr = (int **)malloc(SEQ_BUF_SIZE);
|
||||
FILE *query_f = 0, *target_f = 0, *info_f = 0;
|
||||
// const char *qf_path = "/public/home/zzh/data/sw/q_s.fa";
|
||||
// const char *tf_path = "/public/home/zzh/data/sw/t_s.fa";
|
||||
// const char *if_path = "/public/home/zzh/data/sw/i_s.txt";
|
||||
const char *qf_path = "/public/home/zzh/data/sw/q_m.fa";
|
||||
const char *tf_path = "/public/home/zzh/data/sw/t_m.fa";
|
||||
const char *if_path = "/public/home/zzh/data/sw/i_m.txt";
|
||||
// const char *qf_path = "/public/home/zzh/data/sw/q_m.fa";
|
||||
// const char *tf_path = "/public/home/zzh/data/sw/t_m.fa";
|
||||
// const char *if_path = "/public/home/zzh/data/sw/i_m.txt";
|
||||
query_f = fopen(qf_path, "r");
|
||||
target_f = fopen(tf_path, "r");
|
||||
info_f = fopen(if_path, "r");
|
||||
|
||||
// 每次读取一定量的数据，然后执行，直到处理完所有数据
|
||||
int total_line_num = 0; // 目前处理的总的数据行数
|
||||
int block_line_num = 0; // 当前循环包含的数据行数
|
||||
int i, j;
|
||||
// const int max_read = READ_BUF_SIZE; // 每次最多读取的字符
|
||||
char read_buf[READ_BUF_SIZE]; // 读文件缓存
|
||||
// int ret_code = 0;
|
||||
|
||||
// 初始化info_arr数组
|
||||
i = 0;
|
||||
j = 0;
|
||||
while (1)
|
||||
{
|
||||
if (j > BLOCK_BUF_SIZE)
|
||||
break;
|
||||
info_arr[i] = &info_buf[j];
|
||||
i += 1;
|
||||
j += 3;
|
||||
}
|
||||
|
||||
int score_normal = 0, score_avx2 = 0, score_avx2_u8 = 0;
|
||||
|
||||
while (!feof(target_f))
|
||||
{
|
||||
block_line_num = 0;
|
||||
// target序列一般占用存储最多，先读取target，看一个buf能读多少行，query和info就按照这个行数来读
|
||||
int cur_read_size = 0;
|
||||
while (!feof(target_f) && cur_read_size < BLOCK_BUF_SIZE)
|
||||
{
|
||||
if (fgets(read_buf, READ_BUF_SIZE, target_f) == NULL)
|
||||
break;
|
||||
const int line_size = strlen(read_buf);
|
||||
assert(line_size < READ_BUF_SIZE);
|
||||
++block_line_num;
|
||||
++total_line_num;
|
||||
strncpy(target_arr + cur_read_size, read_buf, line_size);
|
||||
cur_read_size += line_size;
|
||||
// fprintf(stderr, "%d %d \n", line_size, cur_read_size);
|
||||
}
|
||||
|
||||
// 读query
|
||||
cur_read_size = 0;
|
||||
for (i = 0; i < block_line_num; ++i)
|
||||
{
|
||||
if (fgets(read_buf, READ_BUF_SIZE, query_f) == NULL)
|
||||
break;
|
||||
const int line_size = strlen(read_buf);
|
||||
assert(line_size < READ_BUF_SIZE);
|
||||
strncpy(query_arr + cur_read_size, read_buf, line_size);
|
||||
cur_read_size += line_size;
|
||||
}
|
||||
|
||||
// 读info
|
||||
cur_read_size = 0;
|
||||
for (i = 0; i < block_line_num; ++i)
|
||||
{
|
||||
if (fgets(read_buf, READ_BUF_SIZE, info_f) == NULL)
|
||||
break;
|
||||
const int line_size = strlen(read_buf);
|
||||
assert(line_size < READ_BUF_SIZE);
|
||||
sscanf(read_buf, "%d %d %d\n", &info_arr[i][0], &info_arr[i][1], &info_arr[i][2]);
|
||||
cur_read_size += line_size;
|
||||
// fprintf(stderr, "%-8d%-8d%-8d\n", info_arr[i][0], info_arr[i][1], info_arr[i][2]);
|
||||
// fprintf(stderr, "%s\n", read_buf);
|
||||
}
|
||||
|
||||
// 性能测试
|
||||
|
||||
// 普通 sw
|
||||
int cur_query_pos = 0;
|
||||
int cur_target_pos = 0;
|
||||
for (i = 0; i < block_line_num; ++i)
|
||||
{
|
||||
#ifdef SHOW_PERF
|
||||
int64_t start_time = get_mseconds();
|
||||
#endif
|
||||
score_normal += ksw_normal(
|
||||
info_arr[i][0],
|
||||
(uint8_t *)query_arr + cur_query_pos,
|
||||
info_arr[i][1],
|
||||
(uint8_t *)target_arr + cur_target_pos,
|
||||
5, mat, 6, 1, 6, 1, 100, 5, 100,
|
||||
info_arr[i][2],
|
||||
&qle, &tle, >le, &gscore, &max_off[0]);
|
||||
#ifdef SHOW_PERF
|
||||
time_sw_normal += get_mseconds() - start_time;
|
||||
#endif
|
||||
// 更新query和target位置信息
|
||||
cur_query_pos += info_arr[i][0];
|
||||
cur_target_pos += info_arr[i][1];
|
||||
// fprintf(stderr, "%d %d %d %d %d %d %d\n", score_normal, qle, tle, gtle, gscore, max_off[0], max_off[1]);
|
||||
}
|
||||
|
||||
// avx2 sw
|
||||
cur_query_pos = 0;
|
||||
cur_target_pos = 0;
|
||||
for (i = 0; i < block_line_num; ++i)
|
||||
{
|
||||
#ifdef SHOW_PERF
|
||||
int64_t start_time = get_mseconds();
|
||||
#endif
|
||||
score_avx2 += ksw_avx2(
|
||||
info_arr[i][0],
|
||||
(uint8_t *)query_arr + cur_query_pos,
|
||||
info_arr[i][1],
|
||||
(uint8_t *)target_arr + cur_target_pos,
|
||||
0, 5, mat, 6, 1, 6, 1,
|
||||
1, 4,
|
||||
100, 5, 100,
|
||||
info_arr[i][2],
|
||||
&qle, &tle, >le, &gscore, &max_off[0]);
|
||||
#ifdef SHOW_PERF
|
||||
time_sw_avx2 += get_mseconds() - start_time;
|
||||
#endif
|
||||
// 更新query和target位置信息
|
||||
cur_query_pos += info_arr[i][0];
|
||||
cur_target_pos += info_arr[i][1];
|
||||
// fprintf(stderr, "%d %d %d %d %d %d %d\n", score_avx2, qle, tle, gtle, gscore, max_off[0], max_off[1]);
|
||||
}
|
||||
|
||||
// avx2 u8 sw
|
||||
cur_query_pos = 0;
|
||||
cur_target_pos = 0;
|
||||
for (i = 0; i < block_line_num; ++i)
|
||||
{
|
||||
#ifdef SHOW_PERF
|
||||
int64_t start_time = get_mseconds();
|
||||
#endif
|
||||
score_avx2_u8 += ksw_avx2_u8(
|
||||
info_arr[i][0],
|
||||
(uint8_t *)query_arr + cur_query_pos,
|
||||
info_arr[i][1],
|
||||
(uint8_t *)target_arr + cur_target_pos,
|
||||
0, 5, mat, 6, 1, 6, 1,
|
||||
1, 4,
|
||||
100, 5, 100,
|
||||
info_arr[i][2],
|
||||
&qle, &tle, >le, &gscore, &max_off[0]);
|
||||
#ifdef SHOW_PERF
|
||||
time_sw_avx2_u8 += get_mseconds() - start_time;
|
||||
#endif
|
||||
// 更新query和target位置信息
|
||||
cur_query_pos += info_arr[i][0];
|
||||
cur_target_pos += info_arr[i][1];
|
||||
// fprintf(stderr, "%d %d %d %d %d %d %d\n", score_normal, qle, tle, gtle, gscore, max_off[0], max_off[1]);
|
||||
}
|
||||
|
||||
// fprintf(stderr, "%d %d \n", block_line_num, total_line_num);
|
||||
}
|
||||
|
||||
// fprintf(stderr, "%d \n", score_normal);
|
||||
|
||||
#ifdef SHOW_PERF
|
||||
fprintf(stderr, "time_sw_normal: %f s; score: %d\n", time_sw_normal / 1000.0, score_normal);
|
||||
fprintf(stderr, "time_sw_avx2: %f s; score: %d\n", time_sw_avx2 / 1000.0, score_avx2);
|
||||
fprintf(stderr, "time_sw_avx2_u8: %f s; score: %d\n", time_sw_avx2_u8 / 1000.0, score_avx2_u8);
|
||||
#endif
|
||||
|
||||
if (query_f != 0)
|
||||
fclose(query_f);
|
||||
if (target_f != 0)
|
||||
fclose(target_f);
|
||||
if (info_f != 0)
|
||||
fclose(info_f);
|
||||
}
|
||||
Loading…
Reference in New Issue